Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications
D Wang, N Gao, D Liu, J Li… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
[HTML][HTML] Optimization of the model predictive control meta-parameters through reinforcement learning
Abstract Model predictive control (MPC) is increasingly being considered for control of fast
systems and embedded applications. However, MPC has some significant challenges for …
systems and embedded applications. However, MPC has some significant challenges for …
Reinforcement learning for optimization of nonlinear and predictive control
E Bøhn - 2022 - ntnuopen.ntnu.no
Autonomous systems extend upon human capabilities and can be equipped with
superhuman attributes in terms of durability, strength, and perception to name a few, and …
superhuman attributes in terms of durability, strength, and perception to name a few, and …
Combining Model-based and Model-free approaches in achieving sample efficieny in Reinforcement Learning
A Nair - 2022 - fse.studenttheses.ub.rug.nl
Reinforcement Learning is broadly classified into model-free (MF) and model-based (MB)
approaches. While MF approaches have repeatedly proved successful in solving a variety of …
approaches. While MF approaches have repeatedly proved successful in solving a variety of …
Data-Driven Model Predictive Control with Reinforcement Learning Algorithms
H Moradimaryamnegari - bia.unibz.it
In this dissertation, we aim to combine model-based (Model Predictive Control) and model-
free (Neural Network) action value functions for the control of robotic systems and their path …
free (Neural Network) action value functions for the control of robotic systems and their path …